scholarly journals REAL-TIME ASSIMILATION USING A DENSE ARRAY OF DIRECTIONAL WAVE OBSERVATIONS

2018 ◽  
pp. 6
Author(s):  
Pieter Bart Smit ◽  
Tim Janssen ◽  
Wheeler Gans ◽  
Cameron Dunning
Keyword(s):  
Low Cost ◽  
Model Performance ◽  
Data Networks ◽  
Large Numbers ◽  
Independent Observations ◽  
Directional Wave ◽  
Wave Conditions ◽  
Wave Models ◽  
Solar Powered ◽  
Buoy Data

Wave conditions along our coastlines are monitored using networks of wave buoys. Augmented with regional wave now- and hind-casts from operational wave models, these data networks provide detailed regional information of wave conditions providing vital updates of wave conditions for maritime, engineering, recreational and scientific purposes. Currently, the observational networks are mostly used to initiate models and assess model performance, but are usually not directly integrated into the modeling system. Recent work by Crosby et al. (2017) explores the integration of buoy data into models and shows that data assimilation of buoy observations into models can improve predictions and wave hindcasts. The results suggest that assimilation of dense observational networks results in significant and important improvements in model performance. In the current work we leverage these modeling advances with the recent development of low-cost directional wave buoys (such as the Spoondrift Spotter, www.spoondrift.co). The use of low-cost and solar powered instruments allows for much denser long-term arrays of instruments than was previously possible. The availability of large numbers of independent observations, in turn, can provide excellent constrains on models and model boundary conditions.

scholarly journals Multi-Step-Ahead Forecasting of Wave Conditions Based on a Physics-Based Machine Learning (PBML) Model for Marine Operations

2020 ◽  
Vol 8 (12) ◽  
pp. 992
Author(s):  
Mengning Wu ◽  
Christos Stefanakos ◽  
Zhen Gao
Keyword(s):  
Machine Learning ◽  
Learning Algorithm ◽  
High Reliability ◽  
Computational Cost ◽  
Model Performance ◽  
The North ◽  
The North Sea ◽  
Wave Conditions ◽  
Wave Models ◽  
Artificial Neural Network Ann

Short-term wave forecasts are essential for the execution of marine operations. In this paper, an efficient and reliable physics-based machine learning (PBML) model is proposed to realize the multi-step-ahead forecasting of wave conditions (e.g., significant wave height Hs and peak wave period Tp). In the model, the primary variables in physics-based wave models (i.e., the wind forcing and initial wave boundary condition) are considered as inputs. Meanwhile, a machine learning algorithm (artificial neural network, ANN) is adopted to build an implicit relation between inputs and forecasted outputs of wave conditions. The computational cost of this data-driven model is obviously much lower than that of the differential-equation based physical model. A ten-year (from 2001 to 2010) dataset of every three hours at the North Sea center was used to assess the model performance in a small domain. The result reveals high reliability for one-day-ahead Hs forecasts, while that of Tp is slightly lower due to the weaker implicit relationships between the data. Overall, the PBML model can be conceived as an efficient tool for the multi-step-ahead forecasting of wave conditions, and thus has great potential for furthering assist decision-making during the execution of marine operations.

Verification of 2D Wave Spectra Produced by Wave Models

2004 ◽  
Author(s):  
Jose´ Caˆndido ◽  
Henrique Oliveira Pires ◽  
M. Teresa Pontes
Keyword(s):  
Frequency Band ◽  
Wind Wave ◽  
Wave Spectra ◽  
Sea State ◽  
Directional Wave ◽  
Wave Models ◽  
Different Origins ◽  
Buoy Data

In this paper a methodology for assessing the accuracy of full directional wave spectra produced by wind-wave models is presented and tested. This methodology includes graphical and parametric comparisons of model directional spectra against data obtained from directional buoys. Results of the verification of 3rd generation wind-wave models using directional buoy data show that in general the accuracy of model directional results is good. In addition it was found that this methodology is well suited to identify the occurrence of different wave systems in the same sea state, namely swells within the same frequency band but with different origins.

scholarly journals Urban Air Quality Modeling Using Low-Cost Sensor Network and Data Assimilation in the Aburrá Valley, Colombia

Atmosphere ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 91
Author(s):  
Santiago Lopez-Restrepo ◽  
Andres Yarce ◽  
Nicolás Pinel ◽  
O.L. Quintero ◽  
Arjo Segers ◽  
...  
Keyword(s):  
Air Quality ◽  
Data Assimilation ◽  
Transport Model ◽  
Cost Model ◽  
Low Cost ◽  
Model Performance ◽  
Monitoring Network ◽  
Correlation Factor ◽  
Chemical Transport Model ◽  
High Quality

The use of low air quality networks has been increasing in recent years to study urban pollution dynamics. Here we show the evaluation of the operational Aburrá Valley’s low-cost network against the official monitoring network. The results show that the PM2.5 low-cost measurements are very close to those observed by the official network. Additionally, the low-cost allows a higher spatial representation of the concentrations across the valley. We integrate low-cost observations with the chemical transport model Long Term Ozone Simulation-European Operational Smog (LOTOS-EUROS) using data assimilation. Two different configurations of the low-cost network were assimilated: using the whole low-cost network (255 sensors), and a high-quality selection using just the sensors with a correlation factor greater than 0.8 with respect to the official network (115 sensors). The official stations were also assimilated to compare the more dense low-cost network’s impact on the model performance. Both simulations assimilating the low-cost model outperform the model without assimilation and assimilating the official network. The capability to issue warnings for pollution events is also improved by assimilating the low-cost network with respect to the other simulations. Finally, the simulation using the high-quality configuration has lower error values than using the complete low-cost network, showing that it is essential to consider the quality and location and not just the total number of sensors. Our results suggest that with the current advance in low-cost sensors, it is possible to improve model performance with low-cost network data assimilation.

scholarly journals “In Today’s Society, It’s a Necessity”: Latino/a Postsecondary Plans in the College-for-all Era

2021 ◽  
Author(s):  
Sarah Ovink
Keyword(s):  
Higher Education ◽  
San Francisco ◽  
Low Income ◽  
Low Cost ◽  
College Completion ◽  
Socioeconomic Mobility ◽  
Bay Area ◽  
Large Numbers ◽  
East Bay ◽  
College Completion Rates

Latino/a enrollments at U.S. colleges are rapidly increasing. However, Latinos/as remain underrepresented at four-year universities, and college completion rates and household earnings lag other groups’. Yet, little theoretical attention has been paid to the processes that drive these trends, or to what happens when students not traditionally expected to attend college begin to enroll in large numbers. Longitudinal interviews with 50 Latino/a college aspirants in the San Francisco East Bay Area reveal near-universal college enrollment among these mostly low-income youth, despite significant barriers. East Bay Latino/a youth draw on a set of interrelated logics (economic, regional, family/group, college-for-all) supporting their enrollment, because they conclude that higher education is necessary for socioeconomic mobility. In contrast to the predictions of status attainment and rational choice models, these rationally optimistic college aspirants largely ignore known risks, instead focusing on anticipated gains. Given a postrecession environment featuring increasing costs and uncertain employment, this approach led many to enroll in low-cost, less supportive two-year institutions, resulting in long and winding pathways for some. Results suggest that without structural supports, access to college fails to meaningfully redress stratification processes in higher education and the postrecession economy that significantly shape possibilities for mobility.

scholarly journals Grain Yield Estimation in Rice Breeding Using Phenological Data and Vegetation Indices Derived from UAV Images

Agronomy ◽  
2021 ◽  
Vol 11 (12) ◽  
pp. 2439
Author(s):  
Haixiao Ge ◽  
Fei Ma ◽  
Zhenwang Li ◽  
Changwen Du
Keyword(s):  
Grain Yield ◽  
Low Cost ◽  
Vegetation Indices ◽  
Model Performance ◽  
Estimation Accuracy ◽  
Accurate Estimation ◽  
Rice Breeding ◽  
Yield Estimation ◽  
Phenological Data ◽  
Significant Difference

The accurate estimation of grain yield in rice breeding is crucial for breeders to screen and select qualified cultivars. In this study, a low-cost unmanned aerial vehicle (UAV) platform mounted with an RGB camera was carried out to capture high-spatial resolution images of rice canopy in rice breeding. The random forest (RF) regression techniques were used to establish yield models by using (1) only color vegetation indices (VIs), (2) only phenological data, and (3) fusion of VIs and phenological data as inputs, respectively. Then, the performances of RF models were compared with the manual observation and CERES-Rice model. The results indicated that the RF model using VIs only performed poorly for estimating yield; the optimized RF model that combined the use of phenological data and color VIs performed much better, which demonstrated that the phenological data significantly improved the model performance. Furthermore, the yield estimation accuracy of 21 rice cultivars that were continuously planted over three years in the optimal RF model had no significant difference (p > 0.05) with that of the CERES-Rice model. These findings demonstrate that the RF model, by combining phenological data and color Vis, is a potential and cost-effective way to estimate yield in rice breeding.

Simulation of Hurricane Seas in a Multidirectional Wave Basin

1991 ◽  
Vol 113 (3) ◽  
pp. 219-227 ◽  
Author(s):  
A. Cornett ◽  
M. D. Miles
Keyword(s):  
Ocean Waves ◽  
Entropy Method ◽  
Wave Spectra ◽  
Single Wave ◽  
Wave Condition ◽  
Wave Basin ◽  
Wide Range ◽  
Directional Wave ◽  
Wave Conditions ◽  
Multidirectional Wave

This paper describes the generation and verification of four realistic sea states in a multidirectional wave basin, each representing a different storm wave condition in the Gulf of Mexico. In all cases, the degree of wave spreading and the mean direction of wave propagation are strongly dependent on frequency. Two of these sea states represent generic design wave conditions typical of hurricanes and winter storms and are defined by JONSWAP wave spectra and parametric spreading functions. Two additional sea states, representing the specific wave activity during hurricanes Betsy and Carmen, are defined by tabulated hindcast estimates of the directional wave energy spectrum. The Maximum Entropy Method (MEM) of directional wave analysis paired with a single-wave probe/ bi-directional current meter sensor is found to be the most satisfactory method to measure multidirectional seas in a wave basin over a wide range of wave conditions. The accuracy of the wave generation and analysis process is verified using residual directional spectra and numerically synthesized signals to supplement those measured in the basin. Reasons for discrepancy between the measured and target directional wave spectra are explored. By attempting to reproduce such challenging sea states, much has been learned about the limitations of simulating real ocean waves in a multidirectional wave basin, and about techniques which can be used to minimize the associated distortions to the directional spectrum.

scholarly journals Low Cost, Robust, Environmentally Friendly Geopolymer-Mesoporous Carbon Composites for Efficient Solar Powered Steam Generation

2018 ◽  
Vol 28 (47) ◽  
pp. 1803266 ◽  
Author(s):  
Fenghua Liu ◽  
Binyuan Zhao ◽  
Weiping Wu ◽  
Haiyan Yang ◽  
Yuesheng Ning ◽  
...  
Keyword(s):  
Mesoporous Carbon ◽  
Low Cost ◽  
Environmentally Friendly ◽  
Carbon Composites ◽  
Steam Generation ◽  
Solar Powered

scholarly journals PV Based Automatic Irrigation System

2021 ◽  
Vol 9 (12) ◽  
pp. 21-30
Author(s):  
M. Sreenivasulu Naik
Keyword(s):  
Water Conservation ◽  
Single Channel ◽  
Irrigation System ◽  
Low Cost ◽  
Reservoir Water ◽  
Moisture Sensor ◽  
Soil Moisture Sensor ◽  
World Energy ◽  
Human Effort ◽  
Solar Powered

Abstract: In Because of the lack of rains and scarcity of land reservoir water, proper irrigation methods are critical in the field of agriculture. The continuous extraction of water from the earth is lowering the water level, causing a lot of land to slowly come into the unirrigated zones. Another important reason for this is because of unplanned water use, which wastes a significant amount of water. This automatic plant irrigation system is used for this purpose. Solar energy is used to power the system via photovoltaic cells. As a result, there is no need to rely on erratic commercial power. In this digital age, we demand that everything around us be automated, reducing human effort. Electronic circuits are becoming more prevalent, making life easier and simpler in today's world. Energy and water scarcity are two major issues that everyone is dealing with these days. As a result, energy and water conservation are required. The goal is to create a solar-powered prototype that will automatically irrigate the field. Consider how convenient it will be to be able to focus on your next task while your field is being irrigated automatically and at a low cost. No worries about underirrigation or over-irrigation, water waste or expensive electricity, or your busy schedule. Keywords: Arduino Uno-Soil Moisture Sensor Submersible Water Pump - Single Channel Relay - Solar Panel - LCD Display - Buzzer - IDE

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